






"""simple docstring"""







import argparse
import re

import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SamConfig,
    SamImageProcessor,
    SamModel,
    SamProcessor,
    SamVisionConfig,
)


a					:							Tuple    =						{
    """iou_prediction_head.layers.0""": """iou_prediction_head.proj_in""",
    """iou_prediction_head.layers.1""": """iou_prediction_head.layers.0""",
    """iou_prediction_head.layers.2""": """iou_prediction_head.proj_out""",
    """mask_decoder.output_upscaling.0""": """mask_decoder.upscale_conv1""",
    """mask_decoder.output_upscaling.1""": """mask_decoder.upscale_layer_norm""",
    """mask_decoder.output_upscaling.3""": """mask_decoder.upscale_conv2""",
    """mask_downscaling.0""": """mask_embed.conv1""",
    """mask_downscaling.1""": """mask_embed.layer_norm1""",
    """mask_downscaling.3""": """mask_embed.conv2""",
    """mask_downscaling.4""": """mask_embed.layer_norm2""",
    """mask_downscaling.6""": """mask_embed.conv3""",
    """point_embeddings""": """point_embed""",
    """pe_layer.positional_encoding_gaussian_matrix""": """shared_embedding.positional_embedding""",
    """image_encoder""": """vision_encoder""",
    """neck.0""": """neck.conv1""",
    """neck.1""": """neck.layer_norm1""",
    """neck.2""": """neck.conv2""",
    """neck.3""": """neck.layer_norm2""",
    """patch_embed.proj""": """patch_embed.projection""",
    """.norm""": """.layer_norm""",
    """blocks""": """layers""",
}





def 		lowercase__(A		)							->int:




						"""simple docstring"""



						lowercase__							: List[str]=			{}
						state_dict.pop("pixel_mean"   ,						UpperCamelCase__		)
						state_dict.pop("pixel_std"   ,						UpperCamelCase__		)

						lowercase__							: Dict=			R".*.output_hypernetworks_mlps.(\d+).layers.(\d+).*"

						for key, value in state_dict.items():
												for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
																		if key_to_modify in key:
																								lowercase__							: Any=			key.replace(UpperCamelCase__   ,						UpperCamelCase__		)

												if re.match(UpperCamelCase__   ,						UpperCamelCase__		):
																		lowercase__							: List[Any]=			int(re.match(UpperCamelCase__   ,						UpperCamelCase__		).group(2		)		)
																		if layer_nb == 0:
																								lowercase__							: int=			key.replace("layers.0"   ,						"proj_in"		)
																		elif layer_nb == 1:
																								lowercase__							: Dict=			key.replace("layers.1"   ,						"layers.0"		)
																		elif layer_nb == 2:
																								lowercase__							: Dict=			key.replace("layers.2"   ,						"proj_out"		)

												lowercase__							: str=			value

						lowercase__							: List[str]=			model_state_dict[
						    "prompt_encoder.shared_embedding.positional_embedding"
						]

						return model_state_dict





def 		lowercase__(A   ,						A   ,						A   ,						A="ybelkada/segment-anything"		)							->Any:




						"""simple docstring"""



						lowercase__							: Optional[int]=			hf_hub_download(UpperCamelCase__   ,						f'''checkpoints/{model_name}.pth'''		)

						if "sam_vit_b" in model_name:
												lowercase__							: Any=			SamConfig()
						elif "sam_vit_l" in model_name:
												lowercase__							: List[Any]=			SamVisionConfig(
												    hidden_size=1_024   ,						num_hidden_layers=24   ,						num_attention_heads=16   ,						global_attn_indexes=[5, 11, 17, 23]   ,						)

												lowercase__							: List[Any]=			SamConfig(
												    vision_config=UpperCamelCase__   ,						)
						elif "sam_vit_h" in model_name:
												lowercase__							: Union[str, Any]=			SamVisionConfig(
												    hidden_size=1_280   ,						num_hidden_layers=32   ,						num_attention_heads=16   ,						global_attn_indexes=[7, 15, 23, 31]   ,						)

												lowercase__							: Optional[int]=			SamConfig(
												    vision_config=UpperCamelCase__   ,						)

						lowercase__							: Tuple=			torch.load(UpperCamelCase__   ,						map_location="cpu"		)
						lowercase__							: str=			replace_keys(UpperCamelCase__		)

						lowercase__							: str=			SamImageProcessor()

						lowercase__							: Dict=			SamProcessor(image_processor=UpperCamelCase__		)
						lowercase__							: Union[str, Any]=			SamModel(UpperCamelCase__		)

						hf_model.load_state_dict(UpperCamelCase__		)
						lowercase__							: List[Any]=			hf_model.to("cuda"		)

						lowercase__							: Union[str, Any]=			"https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
						lowercase__							: List[Any]=			Image.open(requests.get(UpperCamelCase__   ,						stream=UpperCamelCase__		).raw		).convert("RGB"		)

						lowercase__							: str=			[[[400, 650]]]
						lowercase__							: Union[str, Any]=			[[1]]

						lowercase__							: int=			processor(images=np.array(UpperCamelCase__		)   ,						return_tensors="pt"		).to("cuda"		)

						with torch.no_grad():
												lowercase__							: Tuple=			hf_model(**UpperCamelCase__		)
						lowercase__							: str=			output.iou_scores.squeeze()

						if model_name == "sam_vit_h_4b8939":
												assert scores[-1].item() == 0.579_890_251_159_668

												lowercase__							: str=			processor(
												    images=np.array(UpperCamelCase__		)   ,						input_points=UpperCamelCase__   ,						input_labels=UpperCamelCase__   ,						return_tensors="pt"		).to("cuda"		)

												with torch.no_grad():
																		lowercase__							: str=			hf_model(**UpperCamelCase__		)
												lowercase__							: Dict=			output.iou_scores.squeeze()

												assert scores[-1].item() == 0.9_712_603_092_193_604

												lowercase__							: Any=			((75, 275, 1_725, 850),)

												lowercase__							: Union[str, Any]=			processor(images=np.array(UpperCamelCase__		)   ,						input_boxes=UpperCamelCase__   ,						return_tensors="pt"		).to("cuda"		)

												with torch.no_grad():
																		lowercase__							: List[str]=			hf_model(**UpperCamelCase__		)
												lowercase__							: Optional[Any]=			output.iou_scores.squeeze()

												assert scores[-1].item() == 0.8_686_015_605_926_514

												# Test with 2 points and 1 image.
												lowercase__							: List[Any]=			[[[400, 650], [800, 650]]]
												lowercase__							: str=			[[1, 1]]

												lowercase__							: Tuple=			processor(
												    images=np.array(UpperCamelCase__		)   ,						input_points=UpperCamelCase__   ,						input_labels=UpperCamelCase__   ,						return_tensors="pt"		).to("cuda"		)

												with torch.no_grad():
																		lowercase__							: Optional[int]=			hf_model(**UpperCamelCase__		)
												lowercase__							: Union[str, Any]=			output.iou_scores.squeeze()

												assert scores[-1].item() == 0.9_936_047_792_434_692


if __name__ == "__main__":
			a					:							Optional[Any]    =						argparse.ArgumentParser()
			a					:							List[str]    =						["""sam_vit_b_01ec64""", """sam_vit_h_4b8939""", """sam_vit_l_0b3195"""]
			parser.add_argument(
			    """--model_name""",
			    default="""sam_vit_h_4b8939""",
			    choices=choices,
			    type=str,
			    help="""Path to hf config.json of model to convert""",
			)
			parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
			parser.add_argument(
			    """--push_to_hub""",
			    action="""store_true""",
			    help="""Whether to push the model and processor to the hub after converting""",
			)
			parser.add_argument(
			    """--model_hub_id""",
			    default="""ybelkada/segment-anything""",
			    choices=choices,
			    type=str,
			    help="""Path to hf config.json of model to convert""",
			)

			a					:							Optional[Any]    =						parser.parse_args()

			convert_sam_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub, args.model_hub_id)







'''simple docstring'''




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__UpperCAmelCase					=logging.get_logger(__name__)




class        a__ (  UpperCAmelCase__					):
     lowerCamelCase    :	Union[str, Any]				=["audio_values", "audio_mask"]






     def __init__(					self						:						List[str]    ,							a						:						List[Any]=20_48    ,							a						:						Optional[int]=1    ,							a						:						List[str]=[16, 16]    ,							a						:						int=1_28    ,							a						:						Dict=4_41_00    ,							a						:						str=86    ,							a						:						int=20_48    ,							a						:						int=0.0    ,							**a						:						Dict    ,							):





         """simple docstring"""



         super().__init__(
             feature_size=a    ,							sampling_rate=a    ,							padding_value=a    ,							**a    ,							)

         __lowerCamelCase            =    spectrogram_length
         __lowerCamelCase            =    num_channels
         __lowerCamelCase            =    patch_size
         __lowerCamelCase            =    feature_size // self.patch_size[1]
         __lowerCamelCase            =    n_fft
         __lowerCamelCase            =    sampling_rate // hop_length_to_sampling_rate
         __lowerCamelCase            =    sampling_rate
         __lowerCamelCase            =    padding_value
         __lowerCamelCase            =    mel_filter_bank(
             num_frequency_bins=1 + n_fft // 2    ,							num_mel_filters=a    ,							min_frequency=0.0    ,							max_frequency=2_20_50.0    ,							sampling_rate=a    ,							norm='''slaney'''    ,							mel_scale='''slaney'''    ,							).T






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple    ,							a						:						np.array       ):





         """simple docstring"""



         __lowerCamelCase            =    spectrogram(
             a    ,							window_function(self.n_fft    ,							'''hann'''       )    ,							frame_length=self.n_fft    ,							hop_length=self.hop_length    ,							power=2.0    ,							mel_filters=self.mel_filters.T    ,							log_mel='''dB'''    ,							db_range=80.0    ,							)
         __lowerCamelCase            =    log_spec[:, :-1]
         __lowerCamelCase            =    log_spec - 20.0
         __lowerCamelCase            =    np.clip(log_spec / 40.0    ,							-2.0    ,							0.0       ) + 1.0
         return log_spec






     def __call__(					self						:						Union[str, Any]    ,							a						:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,							a						:						Optional[Union[str, TensorType]] = None    ,							a						:						Optional[bool] = True    ,							a						:						Optional[int] = None    ,							a						:						bool = False    ,							a						:						bool = False    ,							**a						:						int    ,							):





         """simple docstring"""



         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     '''This feature extractor is set to support sampling rate'''
                     f""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
                     f""" with {self.sampling_rate} and not {sampling_rate}."""       )
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''       )

         __lowerCamelCase            =    isinstance(a    ,							np.ndarray       ) and len(raw_speech.shape       ) > 1
         if is_batched_numpy and len(raw_speech.shape       ) > 2:
             raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""       )
         __lowerCamelCase            =    is_batched_numpy or (
             isinstance(a    ,							(list, tuple)       ) and (isinstance(raw_speech[0]    ,							(np.ndarray, tuple, list)       ))
         )
         if is_batched:
             __lowerCamelCase            =    [np.asarray([speech]    ,							dtype=np.floataa       ).T for speech in raw_speech]
         elif not is_batched and not isinstance(a    ,							np.ndarray       ):
             __lowerCamelCase            =    np.asarray(a    ,							dtype=np.floataa       )
         elif isinstance(a    ,							np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
             __lowerCamelCase            =    raw_speech.astype(np.floataa       )
         # always return batch
         if not is_batched:
             __lowerCamelCase            =    [np.asarray([raw_speech]       ).T]

         # Convert audio signals to log mel spectrograms, truncate by time axis
         __lowerCamelCase            =    [
             self._np_extract_fbank_features(waveform.squeeze()       ).T[: self.spectrogram_length] for waveform in raw_speech
         ]
         if isinstance(audio_features[0]    ,							a       ):
             __lowerCamelCase            =    [np.asarray(a    ,							dtype=np.floataa       ) for feature in audio_features]

         # Create audio attention mask
         __lowerCamelCase            =    max(
             [ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len for feature in audio_features]       )  # The maximum number of audio patches in a batch
         if return_attention_mask:
             __lowerCamelCase            =    [
                 (ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [1]
                 + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [0]
                 for feature in audio_features
             ]
             __lowerCamelCase            =    np.array(a       ).astype(np.floataa       )

         # convert into correct format for padding
         __lowerCamelCase            =    max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
         __lowerCamelCase            =    np.ones([len(a       ), 1, max_time_len, self.feature_size]       ).astype(np.floataa       )
         __lowerCamelCase            =    padded_audio_features * self.padding_value
         for i in range(len(a       )       ):
             __lowerCamelCase            =    audio_features[i]
             __lowerCamelCase            =    feature

         # return as BatchFeature
         if return_attention_mask:
             __lowerCamelCase            =    {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
         else:
             __lowerCamelCase            =    {'''audio_values''': padded_audio_features}

         __lowerCamelCase            =    BatchFeature(data=a    ,							tensor_type=a       )
         return encoded_inputs
